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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code>.RFE</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-feature-selection-rfe">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.feature_selection.RFE</span></code></a></li>
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  <div class="section" id="sklearn-feature-selection-rfe">
<h1><a class="reference internal" href="../classes.html#module-sklearn.feature_selection" title="sklearn.feature_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.feature_selection</span></code></a>.RFE<a class="headerlink" href="#sklearn-feature-selection-rfe" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.feature_selection.RFE">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.feature_selection.</code><code class="sig-name descname">RFE</code><span class="sig-paren">(</span><em class="sig-param">estimator</em>, <em class="sig-param">n_features_to_select=None</em>, <em class="sig-param">step=1</em>, <em class="sig-param">verbose=0</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_selection/_rfe.py#L37"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE" title="Permalink to this definition">¶</a></dt>
<dd><p>Feature ranking with recursive feature elimination.</p>
<p>Given an external estimator that assigns weights to features (e.g., the
coefficients of a linear model), the goal of recursive feature elimination
(RFE) is to select features by recursively considering smaller and smaller
sets of features. First, the estimator is trained on the initial set of
features and the importance of each feature is obtained either through a
<code class="docutils literal notranslate"><span class="pre">coef_</span></code> attribute or through a <code class="docutils literal notranslate"><span class="pre">feature_importances_</span></code> attribute.
Then, the least important features are pruned from current set of features.
That procedure is recursively repeated on the pruned set until the desired
number of features to select is eventually reached.</p>
<p>Read more in the <a class="reference internal" href="../feature_selection.html#rfe"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>estimator</strong><span class="classifier">object</span></dt><dd><p>A supervised learning estimator with a <code class="docutils literal notranslate"><span class="pre">fit</span></code> method that provides
information about feature importance either through a <code class="docutils literal notranslate"><span class="pre">coef_</span></code>
attribute or through a <code class="docutils literal notranslate"><span class="pre">feature_importances_</span></code> attribute.</p>
</dd>
<dt><strong>n_features_to_select</strong><span class="classifier">int or None (default=None)</span></dt><dd><p>The number of features to select. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, half of the features
are selected.</p>
</dd>
<dt><strong>step</strong><span class="classifier">int or float, optional (default=1)</span></dt><dd><p>If greater than or equal to 1, then <code class="docutils literal notranslate"><span class="pre">step</span></code> corresponds to the
(integer) number of features to remove at each iteration.
If within (0.0, 1.0), then <code class="docutils literal notranslate"><span class="pre">step</span></code> corresponds to the percentage
(rounded down) of features to remove at each iteration.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, (default=0)</span></dt><dd><p>Controls verbosity of output.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>n_features_</strong><span class="classifier">int</span></dt><dd><p>The number of selected features.</p>
</dd>
<dt><strong>support_</strong><span class="classifier">array of shape [n_features]</span></dt><dd><p>The mask of selected features.</p>
</dd>
<dt><strong>ranking_</strong><span class="classifier">array of shape [n_features]</span></dt><dd><p>The feature ranking, such that <code class="docutils literal notranslate"><span class="pre">ranking_[i]</span></code> corresponds to the
ranking position of the i-th feature. Selected (i.e., estimated
best) features are assigned rank 1.</p>
</dd>
<dt><strong>estimator_</strong><span class="classifier">object</span></dt><dd><p>The external estimator fit on the reduced dataset.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.feature_selection.RFECV.html#sklearn.feature_selection.RFECV" title="sklearn.feature_selection.RFECV"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RFECV</span></code></a></dt><dd><p>Recursive feature elimination with built-in cross-validated selection of the best number of features</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>Allows NaN/Inf in the input if the underlying estimator does as well.</p>
<p class="rubric">References</p>
<dl class="citation">
<dt class="label" id="re310f679c81e-1"><span class="brackets">Re310f679c81e-1</span></dt>
<dd><p>Guyon, I., Weston, J., Barnhill, S., &amp; Vapnik, V., “Gene selection
for cancer classification using support vector machines”,
Mach. Learn., 46(1-3), 389–422, 2002.</p>
</dd>
</dl>
<p class="rubric">Examples</p>
<p>The following example shows how to retrieve the 5 most informative
features in the Friedman #1 dataset.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_friedman1</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">RFE</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVR</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_friedman1</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estimator</span> <span class="o">=</span> <span class="n">SVR</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span> <span class="o">=</span> <span class="n">RFE</span><span class="p">(</span><span class="n">estimator</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span> <span class="o">=</span> <span class="n">selector</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span><span class="o">.</span><span class="n">support_</span>
<span class="go">array([ True,  True,  True,  True,  True, False, False, False, False,</span>
<span class="go">       False])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">selector</span><span class="o">.</span><span class="n">ranking_</span>
<span class="go">array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.decision_function" title="sklearn.feature_selection.RFE.decision_function"><code class="xref py py-obj docutils literal notranslate"><span class="pre">decision_function</span></code></a>(self, X)</p></td>
<td><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.fit" title="sklearn.feature_selection.RFE.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(self, X, y)</p></td>
<td><p>Fit the RFE model and then the underlying estimator on the selected</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.fit_transform" title="sklearn.feature_selection.RFE.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(self, X[, y])</p></td>
<td><p>Fit to data, then transform it.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.get_params" title="sklearn.feature_selection.RFE.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>(self[, deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.get_support" title="sklearn.feature_selection.RFE.get_support"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_support</span></code></a>(self[, indices])</p></td>
<td><p>Get a mask, or integer index, of the features selected</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.inverse_transform" title="sklearn.feature_selection.RFE.inverse_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inverse_transform</span></code></a>(self, X)</p></td>
<td><p>Reverse the transformation operation</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.predict" title="sklearn.feature_selection.RFE.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(self, X)</p></td>
<td><p>Reduce X to the selected features and then predict using the</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.predict_log_proba" title="sklearn.feature_selection.RFE.predict_log_proba"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_log_proba</span></code></a>(self, X)</p></td>
<td><p>Predict class log-probabilities for X.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.predict_proba" title="sklearn.feature_selection.RFE.predict_proba"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict_proba</span></code></a>(self, X)</p></td>
<td><p>Predict class probabilities for X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.score" title="sklearn.feature_selection.RFE.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(self, X, y)</p></td>
<td><p>Reduce X to the selected features and then return the score of the</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.set_params" title="sklearn.feature_selection.RFE.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(self, \*\*params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.feature_selection.RFE.transform" title="sklearn.feature_selection.RFE.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(self, X)</p></td>
<td><p>Reduce X to the selected features.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.feature_selection.RFE.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">estimator</em>, <em class="sig-param">n_features_to_select=None</em>, <em class="sig-param">step=1</em>, <em class="sig-param">verbose=0</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_selection/_rfe.py#L122"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.decision_function">
<code class="sig-name descname">decision_function</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L274"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.decision_function" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the decision function of <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like or sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input samples. Internally, it will be converted to
<code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> and if a sparse matrix is provided
to a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">array, shape = [n_samples, n_classes] or [n_samples]</span></dt><dd><p>The decision function of the input samples. The order of the
classes corresponds to that in the attribute <a class="reference internal" href="../../glossary.html#term-classes"><span class="xref std std-term">classes_</span></a>.
Regression and binary classification produce an array of shape
[n_samples].</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_selection/_rfe.py#L137"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.fit" title="Permalink to this definition">¶</a></dt>
<dd><dl class="simple">
<dt>Fit the RFE model and then the underlying estimator on the selected</dt><dd><p>features.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The training input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like of shape (n_samples,)</span></dt><dd><p>The target values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">**fit_params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L544"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Fit to data, then transform it.</p>
<p>Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">numpy array of shape [n_samples, n_features]</span></dt><dd><p>Training set.</p>
</dd>
<dt><strong>y</strong><span class="classifier">numpy array of shape [n_samples]</span></dt><dd><p>Target values.</p>
</dd>
<dt><strong>**fit_params</strong><span class="classifier">dict</span></dt><dd><p>Additional fit parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">numpy array of shape [n_samples, n_features_new]</span></dt><dd><p>Transformed array.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">deep=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L173"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">mapping of string to any</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.get_support">
<code class="sig-name descname">get_support</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">indices=False</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_selection/_base.py#L26"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.get_support" title="Permalink to this definition">¶</a></dt>
<dd><p>Get a mask, or integer index, of the features selected</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>indices</strong><span class="classifier">boolean (default False)</span></dt><dd><p>If True, the return value will be an array of integers, rather
than a boolean mask.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>support</strong><span class="classifier">array</span></dt><dd><p>An index that selects the retained features from a feature vector.
If <code class="docutils literal notranslate"><span class="pre">indices</span></code> is False, this is a boolean array of shape
[# input features], in which an element is True iff its
corresponding feature is selected for retention. If <code class="docutils literal notranslate"><span class="pre">indices</span></code> is
True, this is an integer array of shape [# output features] whose
values are indices into the input feature vector.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.inverse_transform">
<code class="sig-name descname">inverse_transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_selection/_base.py#L87"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.inverse_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Reverse the transformation operation</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_selected_features]</span></dt><dd><p>The input samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_original_features]</span></dt><dd><p><code class="docutils literal notranslate"><span class="pre">X</span></code> with columns of zeros inserted where features would have
been removed by <a class="reference internal" href="#sklearn.feature_selection.RFE.transform" title="sklearn.feature_selection.RFE.transform"><code class="xref py py-meth docutils literal notranslate"><span class="pre">transform</span></code></a>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L236"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict" title="Permalink to this definition">¶</a></dt>
<dd><dl class="simple">
<dt>Reduce X to the selected features and then predict using the</dt><dd><p>underlying estimator.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_features]</span></dt><dd><p>The input samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>y</strong><span class="classifier">array of shape [n_samples]</span></dt><dd><p>The predicted target values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.predict_log_proba">
<code class="sig-name descname">predict_log_proba</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L316"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict_log_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class log-probabilities for X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_features]</span></dt><dd><p>The input samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>p</strong><span class="classifier">array of shape (n_samples, n_classes)</span></dt><dd><p>The class log-probabilities of the input samples. The order of the
classes corresponds to that in the attribute <a class="reference internal" href="../../glossary.html#term-classes"><span class="xref std std-term">classes_</span></a>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.predict_proba">
<code class="sig-name descname">predict_proba</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L296"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict class probabilities for X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like or sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>The input samples. Internally, it will be converted to
<code class="docutils literal notranslate"><span class="pre">dtype=np.float32</span></code> and if a sparse matrix is provided
to a sparse <code class="docutils literal notranslate"><span class="pre">csr_matrix</span></code>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>p</strong><span class="classifier">array of shape (n_samples, n_classes)</span></dt><dd><p>The class probabilities of the input samples. The order of the
classes corresponds to that in the attribute <a class="reference internal" href="../../glossary.html#term-classes"><span class="xref std std-term">classes_</span></a>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/utils/metaestimators.py#L254"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.score" title="Permalink to this definition">¶</a></dt>
<dd><dl class="simple">
<dt>Reduce X to the selected features and then return the score of the</dt><dd><p>underlying estimator.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_features]</span></dt><dd><p>The input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array of shape [n_samples]</span></dt><dd><p>The target values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">**params</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/base.py#L205"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as pipelines). The latter have parameters of the form
<code class="docutils literal notranslate"><span class="pre">&lt;component&gt;__&lt;parameter&gt;</span></code> so that it’s possible to update each
component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">object</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.feature_selection.RFE.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/feature_selection/_base.py#L61"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.feature_selection.RFE.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Reduce X to the selected features.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_features]</span></dt><dd><p>The input samples.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_r</strong><span class="classifier">array of shape [n_samples, n_selected_features]</span></dt><dd><p>The input samples with only the selected features.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-feature-selection-rfe">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.feature_selection.RFE</span></code><a class="headerlink" href="#examples-using-sklearn-feature-selection-rfe" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="A recursive feature elimination example showing the relevance of pixels in a digit classificati..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_rfe_digits_thumb.png" src="../../_images/sphx_glr_plot_rfe_digits_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py"><span class="std std-ref">Recursive feature elimination</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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